How to automate data cleaning: Step-by-step guide

Cleaning and transforming raw data into a usable analysis-ready format is a necessary yet time-consuming aspect of any data analytics project. 

However, data workers spend as much as 45% of their time on it.

With tools like KNIME, you can turn data cleaning and preparation into a simple and automated process that ensures that you always have data ready for analysis. Let’s explore how.

Data cleaning is one of the most time consuming, low value, and yet necessary parts of all data work. In fact, data cleaning takes up the most time of all aspects of the data lifecycle. 

Data needs to be cleaned so analysts can mine accurate insights that drive the business forward. For companies that want to scale AI initiatives, clean and well-structured datasets are also required, making clean data a prerequisite. 

Let’s take a look at some of the reasons you might want to automate data cleaning.

When data cleaning is a highly manual process, error can creep in – especially when working in spreadsheets. Automating the process almost entirely removes human error from the equation, and creates a clear and repeatable process that increases trust in the data and the insights you derive from it.

When data is already cleaned for you when you start work in the morning, you can reach insights faster, and make quicker decisions. In time-sensitive industries and departments like supply chain, automating data cleaning can deliver a competitive edge. 

Much data cleaning work is relatively simple and tedious. When the boring work is automated, that gives more time for team members to focus on high value tasks that deliver business value. 

Before automating data preparation, we need to get all your data in one place. The first step is to download KNIME Analytics Platform and integrate data from sources across your business, such as databases, spreadsheets, APIs, or cloud storage. 

KNIME offers 300+ connectors to pull in data from these sources automatically. You can import data from flat files (e.g., CSV, Excel), SQL databases, or even web-based data sources via APIs. 

You can also automate this process, ensuring that fresh data is automatically pulled from different locations without any manual intervention.

Once you've gathered the data you want to work with, you can perform a variety of data manipulation tasks, such as adding or removing columns, sorting, filtering, and more — all through visual workflows. 

One helpful thing with KNIME is that you can build your data cleaning workflow once and can automate it every subsequent time, saving hours upon hours of low value data cleaning work.

In KNIME, data can be cleaned and manipulated with simple visual workflows that consist of drag and drop nodes that are connected together and each represent a specific operation like reading, cleaning, transforming, merging, or outputting data. Unlike working in Excel or Python, this makes the data cleaning process completely transparent and explainable. 

As you build your workflow, KNIME suggests the next likely step, which you can add with a single click. Additionally, you can use the KNIME GenAI assistant, K-AI, to build the workflow for you via chat. 

This intuitive setup makes working with data accessible to both non-technical users and data experts, letting them quickly prepare their data for analysis. 

Here are some examples of common data manipulation tasks that you can carry out in minutes in KNIME.

Use nodes like Concatenate or Joiner to bring data into a single dataset. You can also use Value Lookup to add matching values from a dictionary table to a data table based on a lookup column, similar to how you might use a lookup in spreadsheets.

Replace missing values across all columns or on a column-by-column basis with the Missing Value node. Detect and manage duplicate data using the Duplicate Row Filter node.

Remove unnecessary data using Row or Column Filter nodes. For more complex filtering, use rule-based filters. The Table Cropper node allows you to select a range of rows and columns, while the Top k Row Filter helps you retain only the top rows after sorting based on specific criteria.

Group rows by unique values using the GroupBy node. Create pivot tables with the Pivot node, and use the Table Manipulator to rename, filter, and reorder your data.

Convert between data types such as strings, integers, and date/time formats easily.

Take a look at this cheat sheet to learn more about the most popular nodes for data preparation and their functions.

Once you've built your data preparation workflow, you can save it and reuse it whenever needed. You can also share it with your colleagues, so they don't have to start from scratch.

Next, you can schedule your workflows to run automatically using KNIME’s automation features. 

Set your data cleaning process to run daily, weekly, or monthly in just a few clicks. For instance, schedule your data transformation workflow to run every evening and have your cleaned and structured data ready for analysis by the time you start work in the morning.

Now you only need to build the data cleaning and transformation process once and it can be reused forever. 

If you too have been spending the bulk of your time preparing data instead of interpreting it, you now have the opportunity to flip that. Start automating your data preparation work by signing up here. 

Not only does a tool like KNIME automate your data cleaning process, it also ensures the exact same data cleaning steps are performed in the same order every single time – removing human error.

So what are you waiting for? Spend 10 minutes building an automation in KNIME, so you can save countless hours of tedious data cleaning work in the future.